Достижения в области растениеводства и технологий

Абстрактный

Yield and Yield Components of Snap Bean (Phaseolus vulgaris L.) as affected by N and P Fertilizer Rates at Jimma, Southwestern Ethiopia

Abdela Negash, Solomon Tulu and Essubalew Getachew

Snap bean is one of the newly introduced legume crops in Ethiopia and it is an important crop in the provision of food security and earing forging currency. It responds much better to the application inorganic fertilizer especially N and P fertilizers due to lack of nodule forming bacteria in the soil and low in building of fertile soil. However, information concerning the production package of snap bean at Jimma was scares and due to this there is limited evidence in the response of snap bean to inorganic fertilizers (N and P fertilizers) which can increase yield and quality. Cognizant to this fact, the experiment was conducted to realize the effect N and P fertilizers rates on yield and yield components of snap bean at Jimma, Southwestern Ethiopia. The experiment consisted of two factors, five levels of N (0, 41, 82, 123, and 164 kg/ha) and four levels of P (0, 46, 92 and 138 kg/ha) arranged 5 × 4 in randomized complete block design with three replications. N as urea was applied in split application (50% at sowing and 50% at flowering) whereas the full dose of P was applied at sowing as triple super phosphate. There was a significant difference (P ≤ 0.05) for main effects of N and P fertilizer rates for all yield and yield component variables. Out of the whole variables tap root length were significant by the interaction effects of N and P fertilizers rate. The result showed that the maximum marketable pod yield was obtained from 123 kg/ha N and 92 kg/ha P2O5 fertilizer application rates. While, the minimum marketable pod yield was recorded from the application of 0 N and P fertilizer rate. So, form this experiment application of 123 kg/ha N and 92 kg/ha P2O5 increased the marketable pod yield of snap bean at Jimma. Since, the experiment was conducted in a single location and season. So, repeating the experiment for more seasons and similar location would help us draw sound conclusive recommendations to the end users.